scale x-axis correctly for barplot - r

When plotting a stacked barplot using {graphics} I get a problem with the x-axis not scaling correctly, the ticks aren't aligned to the bars properly, leaving the axis too short.
# dummy data
mat <- structure(c(0L, 5L, 7L, 10L, 12L, 14L, 16L, 18L, 20L, 22L, 24L,
26L, 28L, 30L, 32L, 34L, 36L, 38L, 40L, 42L, 44L, 46L, 48L, 50L,
52L, 54L, 56L, 58L, 60L, 62L, 63L, 64L, 0L, 0L, 0L, 3L, 0L, 0L,
1L, 0L, 0L, 0L, 5L, 0L, 1L, 4L, 0L, 9L, 0L, 0L, 1L, 0L, 8L, 0L,
7L, 0L, 1L, 1L, 6L, 0L, 1L, 3L, 4L, 0L, 1L, 1L, 5L, 6L, 1L, 6L,
0L, 0L, 5L, 4L, 1L, 8L, 0L, 1L, 1L, 3L, 1L, 3L, 1L, 0L, 1L, 1L,
1L, 1L, 0L, 3L, 3L, 5L, 4L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 6L, 0L,
11L, 0L, 7L, 0L, 6L, 0L, 0L, 5L, 4L, 0L, 1L, 0L, 1L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 9L, 0L), .Dim = c(32L, 5L
), .Dimnames = list(NULL, c("Time", "Var1", "Var2", "Var3", "Var4"
)))
# barplot
barplot(t(mat[,2:4]), beside=F, legend=levels(mat), col=c("blue",'red','forestgreen','purple'))
# manually assign x-axis
axis(1,at=c(1:32),labels=mat[,1])
Any pointers on this would be highly appreciated. Im not interested in a ggplot2 solution. Thanks!


For your axis, get the coordinates of the barplots first.
bp <-barplot(t(mat[,2:5]), beside=F,
legend = levels(mat), col = c("blue",'red','forestgreen','purple'))
Now use bp for x-tick labels
axis(1,at=bp,labels=mat[,1])
The resulting plot
Also, if you play with the width of your plot window/device, you can get all the labels.

Related

I'm getting the following error code when I run rankabundance from BiodiversityR package

I'm trying to compute the ranked abundances of a community data (site*species matrix) by using rankabundance(df) in the BiodiversityR package. But the following error keeps popping up whenever I try to run it.
Error in `[.data.frame`(pi, i) : undefined columns selected
Can someone please help with what this code means?
I've already specified the column names when sub-setting the data. And the data is also in the right format; I've tried running BCI (from vegan) for the same function and it runs perfectly fine. My data is the same format as BCI.
library(BiodiversityR)
rankabundance(alad2, digits = 1)
This is the code that I'm running, and the data-frame is arranged in a site*species matrix, where sites are rows and species are columns.
Here is the dataframe, alad2:
structure(list(`Alysicarpous sp.1` = c(0L, 0L, 1L, 0L, 0L, 4L,
0L, 0L, 0L, 0L, 0L, 4L), `Alysicarpous sp.2` = c(0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), `Bothriochloa pertusa` = c(0L,
0L, 4L, 0L, 12L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), `Butea monosperma ` = c(0L,
0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), `Chromolaena odorata` = c(0L,
0L, 0L, 1L, 3L, 0L, 0L, 5L, 17L, 4L, 0L, 0L), `Chrysopogon sp.*` = c(62L,
64L, 57L, 68L, 72L, 74L, 72L, 62L, 56L, 67L, 54L, 61L), `Desmodium triflorum` = c(0L,
2L, 7L, 12L, 6L, 12L, 0L, 10L, 13L, 0L, 14L, 8L), `Eragrostis tenuifolia` = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L), `Fimbristylis dichotoma` = c(32L,
38L, 41L, 26L, 38L, 38L, 41L, 20L, 28L, 41L, 31L, 32L), H80 = c(2L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L), `Hemigraphis sp.*` = c(0L,
0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 3L, 0L, 0L), `Ischaemum sp.*` = c(18L,
0L, 18L, 18L, 0L, 18L, 33L, 26L, 12L, 16L, 24L, 23L), `Lantana camara` = c(0L,
0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L), `Leucas aspera` = c(0L,
0L, 0L, 0L, 2L, 2L, 0L, 0L, 1L, 0L, 0L, 0L), `Oldenlandia umbellata` = c(3L,
6L, 9L, 8L, 3L, 0L, 0L, 3L, 6L, 7L, 3L, 0L), `Phyllanthus virgatus` = c(0L, 2L, 9L, 13L, 6L, 7L, 9L, 0L, 0L, 6L, 11L, 8L), `Rungia pectinata` = c(0L,
0L, 0L, 2L, 3L, 3L, 0L, 0L, 0L, 0L, 0L, 0L), `Senagalia pennata` = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L), `Senna spectabilis ` = c(0L,
0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), `Setaria flavida` = c(0L,
0L, 0L, 0L, 11L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), `Setaria pumila` = c(4L,
0L, 13L, 0L, 0L, 0L, 5L, 4L, 7L, 5L, 4L, 7L), `Themeda triandra` = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L)), row.names = c(NA,
-12L), class = c("tbl_df", "tbl", "data.frame"))

You do not have a data frame, but a tibble. Use alad2 <- as.data.frame(alad2) and your code will work.

Update dataframe by Comparing Date field records in a second dataframe and append new records only

I want to compare the Date field of two dataframes and add only the latest records from the second one. The first dataframe has the latest records. These records are updated daily from site. The second one reads the records from a csv file that I saved from the previous day.
data I read from the internet:
df_new<-structure(list(DCounter = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
CCounter = c(125L, 36L, 22L, 17L, 11L, 8L, 4L, 20L, 8L, 3L),
RCounter = c(24L, 33L, 34L, 50L, 33L, 21L, 62L, 10L, 20L, 31L),
CrCounter = c(1L, 1L, 8L, 2L, 2L, 8L, 2L, 3L, 0L, 1L),
Date = c("20/03/2020", "19/03/2020", "18/03/2020", "17/03/2020", "16/03/2020", "15/03/2020", "14/03/2020", "13/03/2020", "12/03/2020","11/03/2020")),
class = "data.frame", row.names = c(NA, 10L))
Format the date field to be Date type and rename field
df_new$Date = as.Date(df_new$Date, format = "%d/%m/%y")
colnames(df_new)<-c("D","C","R","Cr","Date")
#old data- read from csv file has data from yesterday
#----------------------
#df_old <- read.csv("df_Saved.csv",header=T)
df_old<-structure(list(D = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
C = c(6L, 12L, 7L, 11L, 8L, 4L, 20L, 8L, 3L, 4L, 1L, 3L, 3L, 0L, 2L, 0L, 0L, 10L, 1L, 0L, 2L, 17L, 15L, 6L, 5L),
R = c(3L,3L, 0L, 3L, 2L, 2L, 0L, 0L, 3L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
Cr = c(1L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
Date = structure(c(17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 25L, 24L, 23L, 22L, 21L, 20L, 19L, 18L, 7L, 6L, 5L, 4L, 3L, 2L, 1L),
.Label = c("2/24/2020", "2/25/2020", "2/26/2020", "2/27/2020",
"2/28/2020", "2/29/2020", "3/1/2020", "3/10/2020", "3/11/2020", "3/12/2020",
"3/13/2020", "3/14/2020", "3/15/2020", "3/16/20", "3/17/20", "3/18/20",
"3/19/20", "3/2/2020", "3/3/2020", "3/4/2020", "3/5/2020", "3/6/2020", "3/7/2020", "3/8/2020", "3/9/2020"), class = "factor")),
class = "data.frame", row.names = c(NA, -25L))
Get today's date and format it
#--------------
dateToAdd<-format(Sys.time(), "%Y/%m/%d")
#extract ONLY updated dates
df_newExtracted<- with(df_new, df_new[(Date >= dateToAdd), ])
if(df_old$Date[1]< df_newExtracted$Date[1] ){
df_final<-rbind(df_newExtracted,df_old)
cat("Add New records\n")
}else{
df_final<-df_old
cat("Nothing new \n")
}
df_final
write.csv(df_final, "df_Saved.csv", row.names=FALSE)
I couldn't figure out the root cause of the problem, sometimes if the difference in the date one day, it works and sometimes the difference 2 days , it's not working. Sometimes if the df_newExtractedrepresent a date that has not been updated by the site like for example: if we run the code today date and they still haven't update their records, the variable will be empty and crash all calculation.
Some suggest the issue related to writing to csv file and reading csv,which will change the format and make the file unstable, and I should use lubridate, that's why I have added the formatting lines. Any suggestion ?

Kruskal.wallis gives out equal p-values

Friends,
I'm having an issue with the Kruskal wallis test in r, testing for stable seasonality with the Kruskal-wallis test. The p-values tested for each variable are coming out the same. Using Kruskal.test(formula, data = mydata) from the library(stats) package . I'm having a hard time believing that the pvalues would be the same.
My dataset is a monthly dataset with 163 obs, 3 macro economic variables in the model and two seasonal dummies.
I'm testing each independent macro economic variable with the dependent variable in the following way Kruskal.test(y~x, data = mydata). So for the data example below it would be Kruskal.test(pr~mev06_mp_lag2, data = mydata). And repeated for each mev in the dataset. All the pvalues for testing the 3 mev's (mev06_mp_lag2, mev29_lag2, mev108_lag1) comes out to be this output:
data: pr by mev29_lag2
Kruskal-Wallis chi-squared = 162, df = 162, p-value = 0.4852
Here is the data:
structure(list(date = structure(c(28L, 56L, 42L, 97L, 1L, 111L,
83L, 70L, 15L, 151L, 138L, 125L, 29L, 57L, 43L, 98L, 2L, 112L,
84L, 71L, 16L, 152L, 139L, 126L, 30L, 58L, 44L, 99L, 3L, 113L,
85L, 72L, 17L, 153L, 140L, 127L, 31L, 59L, 45L, 100L, 4L, 114L,
86L, 73L, 18L, 154L, 141L, 128L, 32L, 60L, 46L, 101L, 5L, 115L,
87L, 74L, 19L, 155L, 142L, 129L, 33L, 61L, 47L, 102L, 6L, 116L,
88L, 75L, 20L, 156L, 143L, 130L, 34L, 62L, 48L, 103L, 7L, 117L,
89L, 76L, 21L, 157L, 144L, 131L, 35L, 63L, 49L, 104L, 8L, 118L,
90L, 77L, 22L, 158L, 145L, 132L, 36L, 64L, 50L, 105L, 9L, 119L,
91L, 78L, 23L, 159L, 146L, 133L, 37L, 65L, 51L, 106L, 10L, 120L,
92L, 79L, 24L, 160L, 147L, 134L, 38L, 66L, 52L, 107L, 11L, 121L,
93L, 80L, 25L, 161L, 148L, 135L, 39L, 67L, 53L, 108L, 12L, 122L,
94L, 81L, 26L, 162L, 149L, 136L, 40L, 68L, 54L, 109L, 13L, 123L,
95L, 82L, 27L, 163L, 150L, 137L, 41L, 69L, 55L, 110L, 14L, 124L,
96L), .Label = c("01APR2006", "01APR2007", "01APR2008", "01APR2009",
"01APR2010", "01APR2011", "01APR2012", "01APR2013", "01APR2014",
"01APR2015", "01APR2016", "01APR2017", "01APR2018", "01APR2019",
"01AUG2006", "01AUG2007", "01AUG2008", "01AUG2009", "01AUG2010",
"01AUG2011", "01AUG2012", "01AUG2013", "01AUG2014", "01AUG2015",
"01AUG2016", "01AUG2017", "01AUG2018", "01DEC2005", "01DEC2006",
"01DEC2007", "01DEC2008", "01DEC2009", "01DEC2010", "01DEC2011",
"01DEC2012", "01DEC2013", "01DEC2014", "01DEC2015", "01DEC2016",
"01DEC2017", "01DEC2018", "01FEB2006", "01FEB2007", "01FEB2008",
"01FEB2009", "01FEB2010", "01FEB2011", "01FEB2012", "01FEB2013",
"01FEB2014", "01FEB2015", "01FEB2016", "01FEB2017", "01FEB2018",
"01FEB2019", "01JAN2006", "01JAN2007", "01JAN2008", "01JAN2009",
"01JAN2010", "01JAN2011", "01JAN2012", "01JAN2013", "01JAN2014",
"01JAN2015", "01JAN2016", "01JAN2017", "01JAN2018", "01JAN2019",
"01JUL2006", "01JUL2007", "01JUL2008", "01JUL2009", "01JUL2010",
"01JUL2011", "01JUL2012", "01JUL2013", "01JUL2014", "01JUL2015",
"01JUL2016", "01JUL2017", "01JUL2018", "01JUN2006", "01JUN2007",
"01JUN2008", "01JUN2009", "01JUN2010", "01JUN2011", "01JUN2012",
"01JUN2013", "01JUN2014", "01JUN2015", "01JUN2016", "01JUN2017",
"01JUN2018", "01JUN2019", "01MAR2006", "01MAR2007", "01MAR2008",
"01MAR2009", "01MAR2010", "01MAR2011", "01MAR2012", "01MAR2013",
"01MAR2014", "01MAR2015", "01MAR2016", "01MAR2017", "01MAR2018",
"01MAR2019", "01MAY2006", "01MAY2007", "01MAY2008", "01MAY2009",
"01MAY2010", "01MAY2011", "01MAY2012", "01MAY2013", "01MAY2014",
"01MAY2015", "01MAY2016", "01MAY2017", "01MAY2018", "01MAY2019",
"01NOV2006", "01NOV2007", "01NOV2008", "01NOV2009", "01NOV2010",
"01NOV2011", "01NOV2012", "01NOV2013", "01NOV2014", "01NOV2015",
"01NOV2016", "01NOV2017", "01NOV2018", "01OCT2006", "01OCT2007",
"01OCT2008", "01OCT2009", "01OCT2010", "01OCT2011", "01OCT2012",
"01OCT2013", "01OCT2014", "01OCT2015", "01OCT2016", "01OCT2017",
"01OCT2018", "01SEP2006", "01SEP2007", "01SEP2008", "01SEP2009",
"01SEP2010", "01SEP2011", "01SEP2012", "01SEP2013", "01SEP2014",
"01SEP2015", "01SEP2016", "01SEP2017", "01SEP2018"), class = "factor"),
pr = c(0.1691759261, 0.1975689455, 0.1701795466, 0.1889038722,
0.1743304586, 0.1850822209, 0.1725476026, 0.1806130453, 0.1769864586,
0.1546961801, 0.18850436, 0.1695999754, 0.1660947088, 0.1929270116,
0.1629685381, 0.1716883769, 0.1782082767, 0.177316379, 0.1586548395,
0.1816295787, 0.1634939904, 0.1653658139, 0.1669465832, 0.1547769918,
0.17154596, 0.1824150313, 0.1600967574, 0.1819462462, 0.1625842114,
0.1605423212, 0.174298958, 0.16859091, 0.1567519737, 0.1549443922,
0.1528250707, 0.1563427163, 0.1562236709, 0.1544731644, 0.1595362963,
0.1749852828, 0.1536175907, 0.1668984941, 0.1532514745, 0.152745466,
0.1590015917, 0.1500819546, 0.1504755171, 0.1583227453, 0.1546476157,
0.1634331963, 0.1565167637, 0.1699421465, 0.1657200266, 0.1642684245,
0.1675084975, 0.1617848489, 0.1662501795, 0.1648139984, 0.1645302595,
0.169286769, 0.1707244798, 0.1845315559, 0.1752391568, 0.1899788506,
0.1784046029, 0.1842806875, 0.1836403012, 0.1753696341, 0.1738240496,
0.1747609205, 0.1724421753, 0.1803992831, 0.1763816185, 0.187630168,
0.1877238382, 0.1860668525, 0.1854666743, 0.1860146483, 0.1781037416,
0.185259322, 0.1879122146, 0.178520754, 0.1875367517, 0.18694397,
0.1860777227, 0.1979044449, 0.1833497201, 0.192027271, 0.1926325454,
0.1916103719, 0.1851319974, 0.1864458557, 0.1832327814, 0.1808570791,
0.1851145899, 0.1815387272, 0.1870942258, 0.1943564723, 0.1862582923,
0.1907279007, 0.1859213896, 0.1865372709, 0.1898453914, 0.1847275775,
0.1736567497, 0.1771092243, 0.1822902114, 0.1840752276, 0.1892670811,
0.1923250842, 0.1852956789, 0.1917880299, 0.18771724, 0.1857801687,
0.1868263217, 0.1867604143, 0.1824500898, 0.1758283625, 0.1829290332,
0.1808247326, 0.183507277, 0.1852845389, 0.1808714285, 0.1818222883,
0.1755951829, 0.1774808136, 0.1775837234, 0.1696830467, 0.172385402,
0.1694350722, 0.168336944, 0.1680335702, 0.1684147459, 0.1726731413,
0.1633235864, 0.1707780779, 0.1606329755, 0.1634684695, 0.1652849939,
0.15803428, 0.1616158193, 0.1527704105, 0.1584612931, 0.1550232032,
0.1534022945, 0.164970584, 0.1565023361, 0.1622506128, 0.1551517442,
0.1539405645, 0.152548495, 0.1516353176, 0.1523898229, 0.1477241538,
0.1502876518, 0.1515682192, 0.1540217905, 0.1589165786, 0.1531622236,
0.1583882529, 0.1532322761, 0.157552401, 0.1621688871), month = c(12L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L), mon1 = c(0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L), mon3 = c(0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L), mev06_mp_lag2 = c(0.2779810102,
0.1874272639, 0.1332826385, 0.1128640237, 0.1247535199, 0.1545791804,
0.2106891929, 0.2757365926, 0.329455103, 0.3808671396, 0.4450555294,
0.5340975751, 0.5971738413, 0.5881040948, 0.4793350636, 0.3124264887,
0.2197636246, 0.2206435437, 0.3113169675, 0.4196078671, 0.5003884945,
0.5494487995, 0.5369484545, 0.4606922562, 0.3338162715, 0.278520389,
0.3170366404, 0.4156696136, 0.4787532552, 0.4443344043, 0.3681819294,
0.2878537618, 0.2048228841, 0.1251537938, 0.0382989338, -0.058589422,
-0.142185008, -0.153725768, -0.074125689, 0.0484987522, 0.0608517463,
-0.079803144, -0.303655154, -0.429635585, -0.363580402, -0.1573843,
0.0420304555, 0.1835101363, 0.2542206609, 0.2533515836, 0.1774048348,
0.0536834552, -0.031620066, -0.048554527, -0.010029088, 0.0691957026,
0.1865379823, 0.314751579, 0.3867383564, 0.3849543674, 0.3270672177,
0.3352052154, 0.4333568873, 0.5807725419, 0.6594152281, 0.5820169704,
0.4614498827, 0.382189864, 0.3472850124, 0.3700953746, 0.4332794073,
0.5388940866, 0.6346031107, 0.6722549883, 0.6226019329, 0.5308626721,
0.5406836123, 0.652356085, 0.8470071782, 0.9341209812, 0.8264468016,
0.612419938, 0.5006911837, 0.5691599433, 0.7307708771, 0.8473791813,
0.8590757515, 0.7900410964, 0.7171039073, 0.6076028502, 0.5505395263,
0.5661995614, 0.631423817, 0.7324609809, 0.776800689, 0.7461146765,
0.6396693594, 0.5909067989, 0.6163303443, 0.6923212327, 0.7608602548,
0.7385415186, 0.7245230167, 0.735008075, 0.7303155287, 0.7306620594,
0.7216900251, 0.710357153, 0.668241137, 0.6465248078, 0.6386886106,
0.644503099, 0.6750915049, 0.6733980993, 0.707678618, 0.7411667711,
0.7159390625, 0.6659808449, 0.6197029436, 0.5965547889, 0.5673138317,
0.5608362128, 0.5669008884, 0.5795942214, 0.5905982279, 0.556992012,
0.5359266787, 0.5449271219, 0.5753646848, 0.6196930073, 0.6313425488,
0.6047324646, 0.5262327459, 0.4680502206, 0.4339327769, 0.422330442,
0.4388551617, 0.4449027001, 0.4724310877, 0.4603556503, 0.3559313099,
0.2192993453, 0.1752438701, 0.2708768468, 0.4398555582, 0.5419383533,
0.5258750189, 0.4264906744, 0.3512451556, 0.3047050285, 0.3177822041,
0.3703341357, 0.4374805453, 0.5119974656, 0.5479752418, 0.5383546522,
0.4763979544, 0.4418530239, 0.4423212346, 0.4638361889, 0.4725955269,
0.4199050848, 0.3677860365), mev29_lag2 = c(12052.672746,
12155.974991, 12259.977269, 12364.551523, 12471.923335, 12575.751994,
12681.578091, 12792.424151, 12903.799861, 13014.933326, 13125.644747,
13237.759633, 13347.540807, 13456.257594, 13563.261568, 13668.005405,
13772.061616, 13868.872889, 13963.208033, 14057.010446, 14145.406294,
14227.079383, 14301.142959, 14368.046479, 14424.924247, 14471.887375,
14508.019112, 14532.668323, 14547.065728, 14552.236417, 14550.020205,
14541.465439, 14527.537817, 14509.400483, 14488.246542, 14464.991414,
14441.692779, 14419.373969, 14399.416496, 14382.82297, 14369.044585,
14358.108259, 14348.715697, 14340.186543, 14332.550823, 14325.428273,
14318.322395, 14310.559769, 14301.864431, 14291.633935, 14279.435535,
14264.935547, 14247.97805, 14230.01465, 14210.49904, 14189.108376,
14166.881283, 14144.225632, 14121.472414, 14098.568702, 14076.59218,
14055.590158, 14035.983138, 14018.088095, 14001.533115, 13987.079436,
13973.759653, 13961.158726, 13949.839264, 13939.826368, 13931.070165,
13923.347123, 13916.816802, 13911.291278, 13906.706121, 13903.022798,
13900.161493, 13898.209865, 13897.051213, 13896.655547, 13897.047312,
13898.205564, 13900.125572, 13902.837452, 13906.230209, 13910.294112,
13914.960492, 13920.218961, 13926.287609, 13932.889015, 13940.451345,
13949.327157, 13959.352267, 13970.583834, 13983.14564, 13997.391872,
14012.965904, 14030.139859, 14048.917902, 14069.304752, 14091.541249,
14113.971365, 14137.471712, 14162.48361, 14187.783215, 14212.951734,
14237.687089, 14262.119284, 14285.160082, 14306.785799, 14326.567908,
14344.249129, 14360.498045, 14374.927988, 14388.841191, 14403.027623,
14417.285193, 14431.921345, 14447.347759, 14464.280067, 14482.60458,
14503.01009, 14525.873936, 14551.515778, 14580.356316, 14610.776601,
14643.555251, 14679.101052, 14716.763371, 14756.356798, 14797.710201,
14841.323243, 14885.552108, 14930.758122, 14976.563876, 15022.743933,
15070.254048, 15116.300407, 15163.332681, 15212.634721, 15262.129309,
15311.443993, 15360.633228, 15410.700926, 15460.012042, 15508.70943,
15555.948922, 15601.38129, 15647.017242, 15691.593748, 15737.814211,
15784.098257, 15824.336441, 15857.184087, 15890.739854, 15937.050823,
15997.292301, 16049.370568, 16063.033239, 16023.148233, 15962.775179,
15932.931115, 15961.380588), mev108_lag1 = c(3.4265582593,
3.8373450191, 4.1211669551, 4.2500265274, 4.2336477943, 4.1032530543,
3.9050112432, 3.691568661, 3.5215361911, 3.4547437295, 3.5245107487,
3.6740870118, 3.8205614376, 3.9060148228, 3.9500668579, 3.9928147249,
4.056423068, 4.097207087, 4.0423248638, 3.8590572205, 3.6249134397,
3.4534377102, 3.419037145, 3.448572797, 3.4287569276, 3.3235979183,
3.3376619007, 3.7361174237, 4.6156476062, 5.5516500424, 5.9018553329,
5.3364327802, 4.406525535, 3.9641497661, 4.5369688556, 5.6155652665,
6.3806850947, 6.3128039966, 5.8286655665, 5.6572058382, 6.1906323861,
7.0408483819, 7.4827400214, 7.0669869294, 6.1581569245, 5.3936717805,
5.2364436715, 5.4913612016, 5.777206406, 5.8339229216, 5.7719456704,
5.8170713396, 6.1029576358, 6.5263492298, 6.8736849118, 6.9975096947,
6.9363923153, 6.7924979551, 6.6668133872, 6.6299076039, 6.7439828613,
7.0243025303, 7.3370606372, 7.4869066644, 7.3844430207, 7.1374881632,
6.940002926, 6.9245088132, 7.0301738798, 7.1305865095, 7.1405475978,
7.1156467585, 7.1524809409, 7.3303394277, 7.6756343523, 8.1680801673,
8.7542261364, 9.1808145707, 9.1010680729, 8.4114150872, 7.6844861301,
7.7270955321, 8.9146989491, 10.361039125, 10.796323189, 9.4618739177,
7.2049954246, 5.5270537994, 5.2221817889, 5.905531143, 6.7592672119,
7.1298927381, 7.0304213613, 6.697874346, 6.3607611025, 6.1569021347,
6.2001333982, 6.5397429639, 7.0184856606, 7.3825719382, 7.5069332339,
7.4599546294, 7.377008726, 7.3638030204, 7.3988155209, 7.4176473452,
7.3829883718, 7.3415942425, 7.3652515353, 7.492033304, 7.6543284954,
7.7427624077, 7.7070473944, 7.6101649913, 7.5623895662, 7.6286991237,
7.7329248639, 7.7505651547, 7.6137269809, 7.4246691851, 7.337208565,
7.4360967197, 7.5892255476, 7.5910082105, 7.3256377393, 6.9067676469,
6.5375463809, 6.3577677595, 6.320229607, 6.3124546301, 6.2662262884,
6.2427837167, 6.3428922976, 6.6124818018, 6.9249171793, 7.0836464531,
6.9995311857, 6.784745399, 6.6375952256, 6.6797395345, 6.7927792813,
6.775540136, 6.5260699355, 6.2318486432, 6.1687507324, 6.4951667771,
7.0000862167, 7.3264282363, 7.2857205376, 6.9859881738, 6.6532338989,
6.4623367973, 6.4024537545, 6.3988018644, 6.3987025271, 6.4148188331,
6.4801548851, 6.6043861168, 6.7236064103, 6.7473536828, 6.6336225214,
6.4408520391, 6.2759289867), p_pr = c(0.1841979358, 0.1909299357,
0.1800235425, 0.1873193897, 0.1778321909, 0.1771717461, 0.1769871609,
0.1769369574, 0.1767002661, 0.1766514006, 0.1772474365, 0.1786372508,
0.1793958093, 0.1873407005, 0.1744738837, 0.1779058647, 0.1660300916,
0.165123522, 0.1662612377, 0.1675426585, 0.1680743656, 0.1680322376,
0.1668552618, 0.1643117778, 0.1604937471, 0.1674889291, 0.1589809185,
0.1707308583, 0.1656141418, 0.1669016231, 0.1658465865, 0.1626002246,
0.1584857239, 0.1556467109, 0.1550484409, 0.1554116407, 0.1553698903,
0.1642789961, 0.1562188049, 0.1676637554, 0.1607636607, 0.159365876,
0.154912779, 0.1508778098, 0.1504706517, 0.1538985266, 0.1585854408,
0.1628016268, 0.1653325485, 0.1746734474, 0.1636385773, 0.1694169075,
0.1595285254, 0.1602916429, 0.1622777106, 0.1647745096, 0.1677972871,
0.170901438, 0.1726448513, 0.1727558383, 0.1718106875, 0.182016627,
0.1762909312, 0.1891248658, 0.1824141631, 0.1800526397, 0.1767170916,
0.1748339829, 0.1743303929, 0.1752424115, 0.1769369171, 0.17959844,
0.182145123, 0.1926835257, 0.1831830764, 0.190698247, 0.1837433962,
0.1875573393, 0.1922445975, 0.1928025222, 0.1883983926, 0.1831397417,
0.1831222451, 0.1882066078, 0.1932319714, 0.2020834894, 0.1878958952,
0.1907776136, 0.179564677, 0.1783669915, 0.1788699402, 0.1800391448,
0.1813284168, 0.1829512395, 0.1831328753, 0.181735949, 0.1790137171,
0.1875337053, 0.1799754626, 0.191124027, 0.1842840392, 0.1833786054,
0.1825845794, 0.182550754, 0.1822481672, 0.1820347832, 0.1814673532,
0.18082831, 0.1795880318, 0.1882358605, 0.1790916575, 0.1878672726,
0.1797660056, 0.1793430747, 0.1799398102, 0.1807822543, 0.180246357,
0.1788849577, 0.1772437109, 0.1760414846, 0.1749113359, 0.1838871358,
0.1750360156, 0.1836953752, 0.1744313344, 0.1722844661, 0.170542729,
0.1699684655, 0.1702419601, 0.1709120463, 0.1706566897, 0.1694752567,
0.1672817086, 0.175105, 0.1653820849, 0.1735863964, 0.1646891174,
0.1638476083, 0.1636914003, 0.1629671545, 0.1601006771, 0.1561250286,
0.1539170317, 0.1550840353, 0.1586350423, 0.1705586865, 0.1617244458,
0.1681380973, 0.1570702457, 0.1547307475, 0.1537854739, 0.1541593825,
0.155270079, 0.1567753976, 0.1573188283, 0.1566263272, 0.154594785,
0.1625938782, 0.1536205501, 0.1632453909, 0.1552261163, 0.1537721633,
0.1517811103), r_pr = c(-0.01502201, 0.0066390098, -0.009843996,
0.0015844825, -0.003501732, 0.0079104748, -0.004439558, 0.003676088,
0.0002861925, -0.02195522, 0.0112569236, -0.009037275, -0.013301101,
0.0055863112, -0.011505346, -0.006217488, 0.0121781851, 0.0121928571,
-0.007606398, 0.0140869202, -0.004580375, -0.002666424, 9.13213e-05,
-0.009534786, 0.0110522129, 0.0149261022, 0.0011158389, 0.0112153879,
-0.00302993, -0.006359302, 0.0084523714, 0.0059906854, -0.00173375,
-0.000702319, -0.00222337, 0.0009310756, 0.0008537806, -0.009805832,
0.0033174915, 0.0073215274, -0.00714607, 0.0075326181, -0.001661304,
0.0018676562, 0.0085309399, -0.003816572, -0.008109924, -0.004478882,
-0.010684933, -0.011240251, -0.007121814, 0.000525239, 0.0061915012,
0.0039767816, 0.0052307869, -0.002989661, -0.001547108, -0.00608744,
-0.008114592, -0.003469069, -0.001086208, 0.0025149289, -0.001051774,
0.0008539848, -0.00400956, 0.0042280478, 0.0069232096, 0.0005356512,
-0.000506343, -0.000481491, -0.004494742, 0.0008008432, -0.005763504,
-0.005053358, 0.0045407618, -0.004631395, 0.0017232781, -0.001542691,
-0.014140856, -0.0075432, -0.000486178, -0.004618988, 0.0044145066,
-0.001262638, -0.007154249, -0.004179044, -0.004546175, 0.0012496574,
0.0130678684, 0.0132433805, 0.0062620573, 0.0064067109, 0.0019043646,
-0.00209416, 0.0019817146, -0.000197222, 0.0080805087, 0.0068227671,
0.0062828296, -0.000396126, 0.0016373504, 0.0031586655, 0.007260812,
0.0021768236, -0.008591417, -0.004925559, 0.0008228582, 0.0032469176,
0.0096790493, 0.0040892237, 0.0062040214, 0.0039207574, 0.0079512344,
0.006437094, 0.0068865115, 0.0059781601, 0.0022037328, -0.003056595,
0.0056853223, 0.004783248, 0.008595941, 0.0013974031, 0.0058354128,
-0.001873087, 0.0011638485, 0.0051963475, 0.0070409944, -0.000285419,
0.0021434419, -0.001476974, -0.002319746, -0.001441687, 0.0011330373,
-0.002431859, -0.002058499, -0.002808318, -0.004056142, -0.000379139,
0.0015935936, -0.004932874, 0.0015151421, -0.003354618, 0.0045442614,
-6.0832e-05, -0.005232748, -0.005588103, -0.00522211, -0.005887484,
-0.001918502, -0.000790183, -0.001236979, -0.002524065, -0.002880256,
-0.009051244, -0.007031176, -0.005058108, -0.000572995, -0.0036773,
-0.000458327, -0.004857138, -0.00199384, 0.0037802378, 0.0103877768
)), .Names = c("date", "pr", "month", "mon1", "mon3", "mev06_mp_lag2",
"mev29_lag2", "mev108_lag1", "p_pr", "r_pr"), class = "data.frame", row.names = c(NA,
-163L))
Am I missing something with the nuances of this test? Thoughts?

A Kruskal-Wallis test compares the dependent variable across groups defined by the unique values of the independent variable (analogous to one-way ANOVA). Your independent variables are continuous, so each splits your 163 observations into the same 163 different groups, each with one observation. This is why the tests come out the same.
A clue was in the output - the test had 162 degrees of freedom on 163 observations!
Kruskal-Wallis chi-squared = 162, df = 162, p-value = 0.4852
So the Kruskal-Wallis test isn't appropriate here, either you meant to bin your dependent variables first (although a K-W test still wouldn't be right as your groups would be ordered), or use a test for correlation.

Why is R misreading the content of a column in a data.frame?

I have a data frame (sub), where I want to multiply the numeric values in column 2 with a factor which differs depending on the "value" of column 'domain'.
data:
sub <- structure(list(domain = c("Bacteria", "Bacteria", "Bacteria",
"Eukaryota", "Eukaryota", "Eukaryota", "Bacteria", "Bacteria",
"Eukaryota", "Bacteria"), `60781` = c(12471263.2580165, 0, 24942526.516033,
9845734.15106566, 0, 19691468.3021313, 122788742566383, 0, 0,
245577485132767), `60782` = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), `60783` = c(2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L),
`60784` = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), `60785` = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), `60786` = c(5L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 6L), `60787` = c(2L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 5L), `60759` = c(3L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 0L, 8L), `60773` = c(1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L), `60774` = c(0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 4L), `60775` = c(2L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 6L), `60776` = c(2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 2L), `60777` = c(4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
4L), `60778` = c(1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 5L),
`60779` = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 4L), `60780` = c(1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L)), .Names = c("domain",
"60781", "60782", "60783", "60784", "60785", "60786", "60787",
"60759", "60773", "60774", "60775", "60776", "60777", "60778",
"60779", "60780"), row.names = c(4549L, 9581L, 14048L, 17710L,
19822L, 17650L, 15353L, 13170L, 20622L, 157L), class = "data.frame")
Q16S <- structure(list(s = structure(c(10L, 11L, 12L, 13L, 14L, 15L,
16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L), .Label = c("60759",
"60773", "60774", "60775", "60776", "60777", "60778", "60779",
"60780", "60781", "60782", "60783", "60784", "60785", "60786",
"60787"), class = "factor"), q = c(12471263.2580165, 9779600.35102098,
4233335.65669403, 4233335.65669403, 5861610.84202048, 3608701.24759829,
1911945.62045948, 5286624.33414104, 23126648.4362759, 4358019.31046983,
8226827.34243214, 4359062.63714278, 2351302.71868581, 5938544.50162295,
2772726.13977936, 7168230.19241166)), .Names = c("s", "q"), row.names = c(10L,
11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L), class = "data.frame")
Q18S <- structure(list(s = structure(c(10L, 11L, 12L, 13L, 14L, 15L,
16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L), .Label = c("60759",
"60773", "60774", "60775", "60776", "60777", "60778", "60779",
"60780", "60781", "60782", "60783", "60784", "60785", "60786",
"60787"), class = "factor"), q = c(9845734.15106566, 7720737.11922709,
3342107.09739003, 3342107.09739003, 4627587.50685827, 2848974.66915655,
1509430.75299433, 4173650.79011135, 18257880.3444283, 3440541.56089723,
6494863.6913938, 3441365.23984957, 1856291.62001511, 4688324.60654444,
2188994.32087844, 5659129.09927236)), .Names = c("s", "q"), row.names = c(10L,
11L, 12L, 13L, 14L, 15L, 16L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L), class = "data.frame")
Code:
sub[[2]][sub$domain =="Bacteria"] <- sub[[2]]*Q16S$q[1]
sub[[2]][sub$domain =="Eukaryota"] <- sub[[2]]*Q18S$q[1]
Eventually I want to do a for loop - loop'ing over column 11:16 and multiplying different factors as specified in Q16S or Q18S depending on 'domain'.
for (i in 1:16){
qdata[[10+i]][qdata$domain =="Bacteria"] <- qdata[[10+i]]*Q16S$q[i]
qdata[[10+i]][qdata$domain =="Eukaryota"] <- qdata[[10+i]]*Q18S$q[i]
}
However in the out-put are now numbers where there were '0' before and where there was for example '2' there is now multiplied by millions....
I do get a warning message, but I can't interpret it:
Warning message:
In sub[[11]][sub$domain == "Eukaryota"] <- sub[[11]] * Q18S$q[1] :
number of items to replace is not a multiple of replacement length
Any suggestions as to what I am doing wrong?

applying Grouped Median to all columns

I am using the grouped Median function (= Median of grouped data) as given in the following link:
how to calculate the median on grouped dataset?
(solution by A5C1D2H2I1M1N2O1R2T1)
For simplicity I will stick to the example of a salary range and counts of people who make that amount of money. I have following conundrum:
Imagine I am an accountant and I have different categories of employees, so I have the same salary range but 60 columns for salary counts. And I have 6 different companies. So if I were to use this function plainly I would have to repeat the steps 360 times... manually. That is a lot of copy-pasting.
I have tried (my salary range are the row names)
GroupedMedian(1:ncol(mydf), mydf$salary, sep="-")
resulting in the following error:
Error in intervals[1, Midrow] : subscript out of bounds
Does anybody have an idea how to calculate the grouped median on every column and perhaps add it to the table as a row below?
UPDATE As requested dput for my data frame
structure(list(Heu1_C = c(0L, 1L, 13L, 9L, 3L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), Hi1_C = c(0L, 9L, 18L, 10L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), Hi2_C = c(0L, 8L, 10L, 7L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), Hi3_R = c(0L, 0L, 2L, 4L, 5L, 2L, 0L, 0L, 0L, 0L, 0L,
0L), Hi4_I = c(0L, 15L, 9L, 10L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), Hi5_I = c(0L, 4L, 11L, 18L, 2L, 3L, 0L, 0L, 0L, 0L, 0L,
0L), Ke1_C = c(0L, 8L, 15L, 13L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L), Ke2_C = c(0L, 12L, 10L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), Ke3_I = c(0L, 4L, 12L, 8L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), Ke4_I = c(0L, 5L, 12L, 7L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
Ke5_I = c(0L, 0L, 3L, 4L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L),
Ke6_R = c(0L, 0L, 2L, 7L, 4L, 2L, 0L, 0L, 0L, 0L, 0L, 0L),
Ke7_I = c(0L, 9L, 13L, 13L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), Ke8_I = c(0L, 8L, 6L, 13L, 3L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), Ke9_I = c(0L, 6L, 12L, 9L, 2L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), Ke10_S = c(0L, 2L, 5L, 3L, 5L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), Ke11_S = c(0L, 3L, 4L, 5L, 6L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), Ku1_A = c(0L, 1L, 4L, 8L, 8L, 1L, 0L, 0L, 0L, 0L, 0L,
0L), Ku2_C = c(0L, 9L, 12L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), Ku3_I = c(0L, 2L, 8L, 17L, 4L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), Ku4_I = c(1L, 6L, 15L, 12L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), Ku5_I = c(0L, 6L, 14L, 10L, 0L, 0L, 1L, 0L, 0L,
0L, 0L, 0L), Ku6_I = c(0L, 10L, 10L, 8L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L), Ku7_R = c(0L, 4L, 5L, 13L, 3L, 0L, 0L, 0L,
0L, 0L, 0L, 0L), Ku8_R = c(0L, 9L, 9L, 10L, 1L, 0L, 0L, 0L,
0L, 0L, 0L, 0L), Ku9_R = c(0L, 0L, 0L, 3L, 3L, 0L, 0L, 0L,
0L, 0L, 0L, 0L), Ku10_I = c(0L, 4L, 10L, 14L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 0L), Ru1_I = c(0L, 13L, 11L, 11L, 7L, 0L,
0L, 0L, 0L, 0L, 0L, 0L), Ru2_I = c(1L, 8L, 11L, 12L, 1L,
1L, 0L, 0L, 0L, 0L, 0L, 0L), Ru3_C = c(0L, 11L, 13L, 7L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Sch1_C = c(0L, 6L, 7L, 5L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Sch2_I = c(0L, 5L, 8L, 11L,
4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Sch3_S = c(0L, 6L, 11L,
10L, 8L, 1L, 0L, 0L, 0L, 0L, 0L, 0L), Sch4_S = c(0L, 2L,
1L, 2L, 8L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Se1_C = c(0L, 6L,
15L, 14L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Se2_C = c(1L,
9L, 10L, 12L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Se3_C = c(0L,
8L, 9L, 8L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Se4_S = c(1L,
1L, 2L, 12L, 11L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Se5_S = c(0L,
1L, 3L, 6L, 14L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Se6_S = c(0L,
0L, 1L, 6L, 15L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StL1_I = c(0L,
0L, 5L, 10L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StL2_C = c(0L,
5L, 8L, 7L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StL3_S = c(0L,
0L, 0L, 2L, 9L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StL4_S = c(0L,
0L, 0L, 2L, 7L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StN1_C = c(0L,
2L, 12L, 3L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StN2_C = c(0L,
5L, 16L, 10L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StN3_R = c(0L,
1L, 2L, 10L, 9L, 1L, 0L, 0L, 0L, 0L, 0L, 0L), StN4_R = c(0L,
0L, 3L, 9L, 11L, 1L, 0L, 0L, 0L, 0L, 0L, 0L), StN5_R = c(1L,
0L, 0L, 4L, 6L, 1L, 0L, 0L, 0L, 0L, 0L, 0L), StN6_R = c(0L,
0L, 0L, 5L, 13L, 1L, 0L, 0L, 0L, 0L, 0L, 0L), StN7_R = c(0L,
0L, 1L, 4L, 7L, 4L, 0L, 0L, 0L, 0L, 0L, 0L), StN8_S = c(0L,
0L, 1L, 3L, 8L, 2L, 0L, 0L, 0L, 0L, 0L, 0L), StN9_S = c(0L,
2L, 4L, 4L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StW1_C = c(0L,
8L, 12L, 8L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StW2_C = c(0L,
12L, 16L, 8L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StW3_I = c(0L,
15L, 16L, 10L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StW4_I = c(0L,
6L, 13L, 5L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StW5_C = c(0L,
8L, 12L, 12L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StW6_S = c(0L,
5L, 8L, 8L, 7L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), StW7_S = c(0L,
0L, 1L, 5L, 10L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("Heu1_C",
"Hi1_C", "Hi2_C", "Hi3_R", "Hi4_I", "Hi5_I", "Ke1_C", "Ke2_C",
"Ke3_I", "Ke4_I", "Ke5_I", "Ke6_R", "Ke7_I", "Ke8_I", "Ke9_I",
"Ke10_S", "Ke11_S", "Ku1_A", "Ku2_C", "Ku3_I", "Ku4_I", "Ku5_I",
"Ku6_I", "Ku7_R", "Ku8_R", "Ku9_R", "Ku10_I", "Ru1_I", "Ru2_I",
"Ru3_C", "Sch1_C", "Sch2_I", "Sch3_S", "Sch4_S", "Se1_C", "Se2_C",
"Se3_C", "Se4_S", "Se5_S", "Se6_S", "StL1_I", "StL2_C", "StL3_S",
"StL4_S", "StN1_C", "StN2_C", "StN3_R", "StN4_R", "StN5_R", "StN6_R",
"StN7_R", "StN8_S", "StN9_S", "StW1_C", "StW2_C", "StW3_I", "StW4_I",
"StW5_C", "StW6_S", "StW7_S"), class = "data.frame", row.names = c("0 - 1",
"1 - 2", "2 - 3", "3 - 4", "4 - 5", "5 - 6", "6 - 7", "7 - 8",
"8 - 9", "9 - 10", "10 - 11", "11 - 12"))

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